Advanced Binary Classification Recipes

Predict a yes/no outcome per entity — using complex multi-source logic, cross-event joins, calendar-aware conditions, and pandas workarounds.

Common advanced patterns

Multi-source gap analysis — combine timestamps from multiple data sources, sort, and detect inactivity gaps
Cross-event joins — match events across data sources using shared identifiers (order ID, ticket ID, loan ID)
Calendar arithmetic — month-end calculations, weekday detection, fiscal quarter navigation
Proportional analysis — compare activity ratios across time periods or channels
Lifecycle tracking — session counting, new-user milestones, course completion stages
Pandas integration — fall back to pandas DataFrames for complex merge/groupby logic

Recipe	Industry	Advanced Pattern
User Silence Detection	Digital	Multi-source gap analysis
IoT Sensor Offline	IoT	Sorted event gap detection
Mobile Payment Adoption	Fintech	New-user lifecycle
Biometric Login	Banking	Month-end date logic
Product Returns	E-commerce	Cross-event join (deliveries + returns)
Extended Warranty	Retail	Cross-event product matching
Positive Reviews	E-commerce	Cross-event join via extra columns
App Channel Shift	Banking	Proportional analysis, backward intervals
Weekday Purchase	Retail	Calendar weekday logic
Installment Defaults	Finance	Heavy pandas merge/cumcount
In-Game Purchase	Gaming	Session counting, lifecycle
Subscription Churn	Fitness	Multi-condition boolean logic
Course Completion	EdTech	Multi-condition timestamp matching